Rheumatoid Arthritis Pathogenesis, Prediction, and Prevention: An Emerging Paradigm Shift
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is currently diagnosed and treated when an individual presents with signs and symptoms of inflammatory arthritis (IA) as well as other features, such as autoantibodies and/or imaging findings, that provide sufficient confidence that the individual has RA-like IA (e.g., meeting established classification criteria) that warrants therapeutic intervention. However, it is now known that there is a stage of seropositive RA during which circulating biomarkers and other factors (e.g., joint symptoms) can be used to predict if and when an individual who does not currently have IA may develop future clinically apparent IA and classifiable RA. Indeed, the discovery of the "pre-RA" stage of seropositive disease has led to the development of several clinical trials in which individuals are studied to identify ways to delay or prevent the onset of clinically apparent IA/RA. This review focuses on several issues pertinent to understanding the prevention of RA. These include discussion of the pathogenesis of pre-RA development, prediction of the likelihood and timing of future classifiable RA, and a review of completed and ongoing clinical trials in RA prevention. Furthermore, this review discusses challenges and opportunities to be addressed to effect a paradigm shift in RA, where in the near future, proactive risk assessment focused on prevention of RA will become a public health strategy in much the same manner as cardiovascular disease is managed today.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it